from sklearn.svm import SVC
import time
import numpy
t=time.time()
svm_classifier = SVC(kernel='rbf',C=100,tol=1e-3,gamma=10,max_iter=3000,verbose=True)
# data=numpy.loadtxt("./testSet.txt",delimiter="\t")
# # pyplot.scatter(data[:,0],data[:,1],c=data[:,-1],edgecolors="red",linewidths=1.5,alpha=0.7)
# # pyplot.show()
# label=data[:,-1]
# feature=data[:,:-1]
from sklearn.datasets import make_circles

feature, label = make_circles(n_samples=500, noise=0.05, random_state=42,factor=0.1)
svm_classifier.fit(feature,label)
print(time.time()-t)
print(svm_classifier.intercept_)